Backstory
On October 11, 2022, I received a rather interesting email from the Aggie Research Program, recruiting students for a project called FOODS (Fully Optimized Organically-Inspired Degradable Structures). In it, was a meme starring Forky, who would later go on to become the unofficial mascot of our project. Naturally, I applied, and a few weeks later, I was on my first ever research project.
FEA analyses of our partially-optimized fork
A Crash Course
The original team was composed of 4 undergrads - Archit, Roberto, Dian, and myself - none of whom had any experience with FEA (finite element analysis). So our advisors, Hannah and Jared (both PhD candidates under MAESTRO lab at the time), started us off with an FEA crash course.
We were introduced to a software called Abaqus and began learning how to go through the whole FEA pipeline - and eventually, how to automate it all using Python scripting. By the end of the Fall 2022 semester, I had put together a full-blown fork script - from modeling, all the way to analysis.
Research Begins
When we returned from winter break, it was time to begin the actual research. Essentially, our goal was to design a plastic fork with optimal biodegradability - by maximizing surface-area-to-volume ratio (SA:V) - while using FEA to ensure that the design wouldn't break.
Since I had progressed the furthest with my FEA skills, I stepped up to lead our effort. First, I defined a fork model with 44 parameters - the idea being that those parameters could be adjusted to find an optimal design. Using PLA as our material, I then created the parametrized fork in Abaqus, set up 2 critical load cases for analysis, generated meshes, and automated it with a Python script.
The meme that started it all
Now, optimizing all 44 parameters would have been impossible, so we had to first narrow it down to the ones with the greatest impact on SA:V and structural integrity. Through this DOE (Design of Experiments) process, I generated a slew of factor effects plots, ran a 2⁷ full factorial analysis, and ultimately, came up with 6 critical parameters.
Optimization Time
With those critical parameters in hand, I could now start running optimization studies using scipy.optimize. The name of the game? Find a fork that maximizes SA:V while not failing under the specified load cases. Unfortunately, this was not as easy as it sounded.
The model and design space had to be constantly refined and improved upon. The optimization algorithm itself (Nelder-Mead) required an initial guess, so there was the conundrum of choosing the right initial guess. If we didn't, the algorithm would just find the nearest local maximum - not, in all likelihood, the global maximum that we were seeking.
The solution I came up with was to run a 3⁶ full factorial analysis, plus a differential evolution optimizer, in order to explore the design space thoroughly and figure out the best initial guess. Now I could simply run that initial guess back through Nelder-Mead, and voila!
By October 2023, we had our partially-optimized fork - 3.4 times the SA:V of the COTS (commercial off-the-shelf) baseline fork that we had started with. Why partially-optimized? Well, that brings us to the present day.
Parameterized fork sketch
Work In Progress
The next step in the process was to run topology optimization on our partially-optimized fork, in order to figure out where further material could be removed. In other words, it was time to add some holes.
The topology optimization has already been run, but at the moment, I am still working to incorporate those results into the model and rerun our optimization. Once that is done, we will finally have our fully-optimized fork.
At the same time, we've also been trying to 3D print our designs for testing. Till now, it has been failure after failure (the main issue being that our forks have very low thicknesses). But we have recently secured a better printer, so fingers crossed, my teammates will be able to conduct their long-awaited failure and degradation tests.
Speaking of the team, there have been some changes since the original group. Hannah has since finished her PhD, with Mason taking her place. Archit and Dian unfortunately had to leave the team, and we have since brought on Favour, Jacob, and Ayman.
Despite the personnel shifts, busy schedules, and a number of other challenges that we've faced, the project goes on. Perhaps, once we finish our optimization, wrap up our testing, and write our paper, Team Forky will at last come to an end. But we shall see.
COTS baseline vs partially-optimized fork